DFT/TDDFT calculations of geometry optimization, electronic structure and spectral properties of clevudine and telbivudine for treatment of chronic hepatitis B.

Chronic hepatitis B remains a worldwide health concern. Presently, many drugs, such as Clevudine and Telbivudine, are recommended for the treatment of chronic hepatitis B disease. For this purpose, the quantum chemical analysis of ELUMO-HOMO (Egap), ionization potential (IP), electron affinity (EA), electronegativity (EN), chemical hardness (η), chemical potential (µ), chemical softness (S), electrophilicity index (ω), electron accepting capability (ω+), electron-donating capability (ω-), Nucleophilicity index (N), additional electronic charge (∆Nmax), Optical softness (σ0) and Dipole Moment, IR and UV-Vis spectra, molecular electrostatic potential (MEP) profile, Mulliken charge analysis, natural bond orbital (NBO) were examined in this study. The dipole moment of the compounds suggests their binding pose and predicted binding affinity. The electrophilic and nucleophilic regions were identified, and techniques such as NBO, UV-Vis, and IR were used to gain insights into the molecular structure, electronic transitions, and potential drug design for Hepatitis B treatment. Calculations for this study were carried out using the Gaussian 09 program package coupled with the DFT/TDDFT technique. The hybrid B3LYP functional method and the 6-311++G(d, p) basis set were used for the calculations.

Quantum simulation of Pauli channels and dynamical maps: Algorithm and implementation.

Pauli channels are fundamental in the context of quantum computing as they model the simplest kind of noise in quantum devices. We propose a quantum algorithm for simulating Pauli channels and extend it to encompass Pauli dynamical maps (parametrized Pauli channels). A parametrized quantum circuit is employed to accommodate for dynamical maps. We also establish the mathematical conditions for an N-qubit transformation to be achievable using a parametrized circuit where only one single-qubit operation depends on the parameter. The implementation of the proposed circuit is demonstrated using IBM's quantum computers for the case of one qubit, and the fidelity of this implementation is reported.

Origin of Enantioselectivity in Engineered Cytochrome c-Catalyzed Carbon-Radical FePP Hydrolysis Revealed Using QM/MM (ABEEM Polarizable Force Field) and MD Simulations.

The origin of highly efficient asymmetric aminohydroxylation of styrene catalyzed by engineered cytochrome c is investigated by the developed Atom-Bond Electronegativity Equalization Method polarizable force field (ABEEM PFF), which is a combined outcome of electronic and steric effects. Model molecules were used to establish the charge parameters of the ABEEM PFF, for which the bond-stretching and angle-bending parameters were obtained by using a combination of modified Seminario and scan methods. The interactions between carbon-radical Fe-porphyrin (FePP) and waters are simulated by molecular dynamics, which shows a clear preference for the pre-R over the pre-S. This preference is attributed to the hydrogen-bond between the mutated 100S and 101P residues as well as van der Waals interactions, enforcing a specific conformation of the carbon-radical FePP complex within the binding pocket. Meanwhile, the hydrogen-bond between water and the nitrogen atom in the active intermediate dictates the stereochemical outcome. Quantum mechanics/molecular mechanics (QM/MM (ABEEM PFF)) and free-energy perturbation calculations elucidate that the 3RTS is characterized by sandwich-like structure among adjacent amino acid residues, which exhibits greater stability than crowed arrangement in 3STS and enables the R enantiomer to form more favorably. Thus, this study provides mechanistic insight into the catalytic reaction of hemoproteins.

The influence of model building schemes and molecular dynamics sampling on QM-cluster models: the chorismate mutase case study.

Most QM-cluster models of enzymes are constructed based on X-ray crystal structures, which limits comparison to in vivo structure and mechanism. The active site of chorismate mutase from Bacillus subtilis and the enzymatic transformation of chorismate to prephenate is used as a case study to guide construction of QM-cluster models built first from the X-ray crystal structure, then from molecular dynamics (MD) simulation snapshots. The Residue Interaction Network ResidUe Selector (RINRUS) software toolkit, developed by our group to simplify and automate the construction of QM-cluster models, is expanded to handle MD to QM-cluster model workflows. Several options, some employing novel topological clustering from residue interaction network (RIN) information, are evaluated for generating conformational clustering from MD simulation. RINRUS then generates a statistical thermodynamic framework for QM-cluster modeling of the chorismate mutase mechanism via refining 250 MD frames with density functional theory (DFT). The 250 QM-cluster models sampled provide a mean ΔG‡ of 10.3 ± 2.6 kcal mol-1 compared to the experimental value of 15.4 kcal mol-1 at 25 °C. While the difference between theory and experiment is consequential, the level of theory used is modest and therefore "chemical" accuracy is unexpected. More important are the comparisons made between QM-cluster models designed from the X-ray crystal structure versus those from MD frames. The large variations in kinetic and thermodynamic properties arise from geometric changes in the ensemble of QM-cluster models, rather from the composition of the QM-cluster models or from the active site-solvent interface. The findings open the way for further quantitative and reproducible calibration in the field of computational enzymology using the model construction framework afforded with the RINRUS software toolkit.

Light-wave-controlled Haldane model in monolayer hexagonal boron nitride.

In recent years, the stacking and twisting of atom-thin structures with matching crystal symmetry has provided a unique way to create new superlattice structures in which new properties emerge1,2. In parallel, control over the temporal characteristics of strong light fields has allowed researchers to manipulate coherent electron transport in such atom-thin structures on sublaser-cycle timescales3,4. Here we demonstrate a tailored light-wave-driven analogue to twisted layer stacking. Tailoring the spatial symmetry of the light waveform to that of the lattice of a hexagonal boron nitride monolayer and then twisting this waveform result in optical control of time-reversal symmetry breaking5 and the realization of the topological Haldane model6 in a laser-dressed two-dimensional insulating crystal. Further, the parameters of the effective Haldane-type Hamiltonian can be controlled by rotating the light waveform, thus enabling ultrafast switching between band structure configurations and allowing unprecedented control over the magnitude, location and curvature of the bandgap. This results in an asymmetric population between complementary quantum valleys that leads to a measurable valley Hall current7, which can be detected by optical harmonic polarimetry. The universality and robustness of our scheme paves the way to valley-selective bandgap engineering on the fly and unlocks the possibility of creating few-femtosecond switches with quantum degrees of freedom.

Modeling Zinc Complexes Using Neural Networks.

Understanding the energetic landscapes of large molecules is necessary for the study of chemical and biological systems. Recently, deep learning has greatly accelerated the development of models based on quantum chemistry, making it possible to build potential energy surfaces and explore chemical space. However, most of this work has focused on organic molecules due to the simplicity of their electronic structures as well as the availability of data sets. In this work, we build a deep learning architecture to model the energetics of zinc organometallic complexes. To achieve this, we have compiled a configurationally and conformationally diverse data set of zinc complexes using metadynamics to overcome the limitations of traditional sampling methods. In terms of the neural network potentials, our results indicate that for zinc complexes, partial charges play an important role in modeling the long-range interactions with a neural network. Our developed model outperforms semiempirical methods in predicting the relative energy of zinc conformers, yielding a mean absolute error (MAE) of 1.32 kcal/mol with reference to the double-hybrid PWPB95 method.

Biomarker discovery with quantum neural networks: a case-study in CTLA4-activation pathways.

BACKGROUND: Biomarker discovery is a challenging task due to the massive search space. Quantum computing and quantum Artificial Intelligence (quantum AI) can be used to address the computational problem of biomarker discovery from genetic data. METHOD: We propose a Quantum Neural Networks architecture to discover genetic biomarkers for input activation pathways. The Maximum Relevance-Minimum Redundancy criteria score biomarker candidate sets. Our proposed model is economical since the neural solution can be delivered on constrained hardware. RESULTS: We demonstrate the proof of concept on four activation pathways associated with CTLA4, including (1) CTLA4-activation stand-alone, (2) CTLA4-CD8A-CD8B co-activation, (3) CTLA4-CD2 co-activation, and (4) CTLA4-CD2-CD48-CD53-CD58-CD84 co-activation. CONCLUSION: The model indicates new genetic biomarkers associated with the mutational activation of CLTA4-associated pathways, including 20 genes: CLIC4, CPE, ETS2, FAM107A, GPR116, HYOU1, LCN2, MACF1, MT1G, NAPA, NDUFS5, PAK1, PFN1, PGAP3, PPM1G, PSMD8, RNF213, SLC25A3, UBA1, and WLS. We open source the implementation at: https://github.com/namnguyen0510/Biomarker-Discovery-with-Quantum-Neural-Networks .

One-dimensional proximity superconductivity in the quantum Hall regime.

Extensive efforts have been undertaken to combine superconductivity and the quantum Hall effect so that Cooper-pair transport between superconducting electrodes in Josephson junctions is mediated by one-dimensional edge states1-6. This interest has been motivated by prospects of finding new physics, including topologically protected quasiparticles7-9, but also extends into metrology and device applications10-13. So far it has proven challenging to achieve detectable supercurrents through quantum Hall conductors2,3,6. Here we show that domain walls in minimally twisted bilayer graphene14-18 support exceptionally robust proximity superconductivity in the quantum Hall regime, allowing Josephson junctions to operate in fields close to the upper critical field of superconducting electrodes. The critical current is found to be non-oscillatory and practically unchanging over the entire range of quantizing fields, with its value being limited by the quantum conductance of ballistic, strictly one-dimensional, electronic channels residing within the domain walls. The system described is unique in its ability to support Andreev bound states at quantizing fields and offers many interesting directions for further exploration.

Quantum mechanical analysis of yttrium-stabilized zirconia and alumina: implications for mechanical performance of esthetic crowns.

BACKGROUND: Yttrium-stabilized zirconia (YSZ) and alumina are the most commonly used dental esthetic crown materials. This study aimed to provide detailed information on the comparison between yttrium-stabilized zirconia (YSZ) and alumina, the two materials most often used for esthetic crowns in dentistry. METHODOLOGY: The ground-state energy of the materials was calculated using the Cambridge Serial Total Energy Package (CASTEP) code, which employs a first-principles method based on density functional theory (DFT). The electronic exchange-correlation energy was evaluated using the generalized gradient approximation (GGA) within the Perdew (Burke) Ernzerhof scheme. RESULTS: Optimization of the geometries and investigation of the optical properties, dynamic stability, band structures, refractive indices, and mechanical properties of these materials contribute to a holistic understanding of these materials. Geometric optimization of YSZ provides important insights into its dynamic stability based on observations of its crystal structure and polyhedral geometry, which show stable configurations. Alumina exhibits a distinctive charge, kinetic, and potential (CKP) geometry, which contributes to its interesting structural framework and molecular-level stability. The optical properties of alumina were evaluated using pseudo-atomic computations, demonstrating its responsiveness to external stimuli. The refractive indices, reflectance, and dielectric functions indicate that the transmission of light by alumina depends on numerous factors that are essential for the optical performance of alumina as a material for esthetic crowns. The band structures of both the materials were explored, and the band gap of alumina was determined to be 5.853 eV. In addition, the band structure describes electronic transitions that influence the conductivity and optical properties of a material. The stability of alumina can be deduced from its bandgap, an essential property that determines its use as a dental material. Refractive indices are vital optical properties of esthetic crown materials. Therefore, the ability to understand their refractive-index graphs explains their transparency and color distortion through how the material responds to light..The regulated absorption characteristics exhibited by YSZ render it a highly attractive option for the development of esthetic crowns, as it guarantees minimal color distortion. CONCLUSION: The acceptability of materials for esthetic crowns is strongly determined by mechanical properties such as elastic stiffness constants, Young's modulus, and shear modulus. YSZ is a highly durable material for dental applications, owing to its superior mechanical strength.

Computational insights into the binding modes, keto-enol tautomerization and stereo-electronically controlled decarboxylation of oxaloacetate in the active site of macrophomate synthase.

Oxaloacetic acid (OAA) is a ß-ketocarboxylic acid, which plays an important role as an intermediate in some metabolic pathways, including the tricarboxylic acid cycle, gluconeogenesis and fatty acid biosynthesis. Animal studies have indicated that supplementing oxaloacetic acid shows an increase of lifespan and other substantial health benefits including mitochondrial DNA protection, and protection of retinal, neural and pancreatic tissues. Most of the chemical transformations of OAA in the metabolic pathways have been extensively studied; however, the understanding of decarboxylation of OAA at the atomic level is relatively lacking. Here, we carried out MD simulations and combined quantum mechanical/molecular mechanical (QM/MM) calculations as an example to systematically elucidate the binding modes, keto-enol tautomerization and decarboxylation of OAA in the active site of macrophomate synthase (MPS), which is a Mg(II)-dependent bifunctional enzyme that catalyzes both the decarboxylation of OAA and [4+2] cycloaddition of 2-pyrone with the decarboxylated intermediate of OAA (pyruvate enolate). On the basis of our calculations, it was found that the Mg2+-coordinated oxaloacetate may exist in enol forms and keto forms. The four keto forms can be transformed into each other by simply rotating the C2-C3 single bond, nevertheless, the keto-enol tautomerization strictly requires the assistance of pocket water molecules. In addition, the decarboxylation is stereo-electronically controlled, i.e., it is the relative orientation of the terminal carboxyl anion that determines the rate of decarboxylation. As such, the chemistry of oxaloacetate in the active site of MPS is complex. On one hand, the most stable binding mode (K-I) may undergo enol-keto tautomerization to isomerize to the enol form, which may further react with the second substrate; on the other hand, K-I may isomerize to another binding mode K-II to proceed decarboxylation to generate pyruvate enolate and CO2. Starting from K-I, the enol-keto tautomerization corresponds to a barrier of 16.2 kcal mol-1, whereas the decarboxylation is associated with an overall barrier of 19.7 kcal mol-1. These findings may provide useful information for understanding the chemistry of OAA and the catalysis of related enzymes, and they are basically in agreement with the available experimental kinetic data.

New classifications for quantum bioinformatics: Q-bioinformatics, QCt-bioinformatics, QCg-bioinformatics, and QCr-bioinformatics.

Bioinformatics has revolutionized biology and medicine by using computational methods to analyze and interpret biological data. Quantum mechanics has recently emerged as a promising tool for the analysis of biological systems, leading to the development of quantum bioinformatics. This new field employs the principles of quantum mechanics, quantum algorithms, and quantum computing to solve complex problems in molecular biology, drug design, and protein folding. However, the intersection of bioinformatics, biology, and quantum mechanics presents unique challenges. One significant challenge is the possibility of confusion among scientists between quantum bioinformatics and quantum biology, which have similar goals and concepts. Additionally, the diverse calculations in each field make it difficult to establish boundaries and identify purely quantum effects from other factors that may affect biological processes. This review provides an overview of the concepts of quantum biology and quantum mechanics and their intersection in quantum bioinformatics. We examine the challenges and unique features of this field and propose a classification of quantum bioinformatics to promote interdisciplinary collaboration and accelerate progress. By unlocking the full potential of quantum bioinformatics, this review aims to contribute to our understanding of quantum mechanics in biological systems.

Systematic QM/MM Study for Predicting 31P NMR Chemical Shifts of Adenosine Nucleotides in Solution and Stages of ATP Hydrolysis in a Protein Environment.

NMR (nuclear magnetic resonance) spectroscopy allows for important atomistic insights into the structure and dynamics of biological macromolecules; however, reliable assignments of experimental spectra are often difficult. Herein, quantum mechanical/molecular mechanical (QM/MM) calculations can provide crucial support. A major problem for the simulations is that experimental NMR signals are time-averaged over much longer time scales, and since computed chemical shifts are highly sensitive to local changes in the electronic and structural environment, sufficiently large averages over representative structural ensembles are essential. This entails high computational demands for reliable simulations. For NMR measurements in biological systems, a nucleus of major interest is 31P since it is both highly present (e.g., in nucleic acids) and easily observable. The focus of our present study is to develop a robust and computationally cost-efficient framework for simulating 31P NMR chemical shifts of nucleotides. We apply this scheme to study the different stages of the ATP hydrolysis reaction catalyzed by p97. Our methodology is based on MM molecular dynamics (MM-MD) sampling, followed by QM/MM structure optimizations and NMR calculations. Overall, our study is one of the most comprehensive QM-based 31P studies in a protein environment and the first to provide computed NMR chemical shifts for multiple nucleotide states in a protein environment. This study sheds light on a process that is challenging to probe experimentally and aims to bridge the gap between measured and calculated NMR spectroscopic properties.

The causal axioms of algebraic quantum field theory: A diagnostic.

Algebraic quantum field theory (AQFT) puts forward three "causal axioms" that aim to characterize the theory as one that implements relativistic causation: the spectrum condition, microcausality, and primitive causality. In this paper, I aim to show, in a minimally technical way, that none of them fully explains the notion of causation appropriate for AQFT because they only capture some of the desiderata for relativistic causation I state or because it is often unclear how each axiom implements its respective desideratum. After this diagnostic, I will show that a fourth condition, local primitive causality (LPC), fully characterizes relativistic causation in the sense of fulfilling all the relevant desiderata. However, it only encompasses the virtues of the other axioms because it is implied by them, as I will show from a construction by Haag and Schroer (1962). Since the conjunction of the three causal axioms implies LPC and other important results in QFT that LPC does not imply, and since LPC helps clarify some of the shortcomings of the three axioms, I advocate for a holistic interpretation of how the axioms characterize the causal structure of AQFT against the strategy in the literature to rivalize the axioms and privilege one among them.

From S-matrix theory to strings: Scattering data and the commitment to non-arbitrariness.

The early history of string theory is marked by a shift from strong interaction physics to quantum gravity. The first string models and associated theoretical framework were formulated in the late 1960s and early 1970s in the context of the S-matrix program for the strong interactions. In the mid-1970s, the models were reinterpreted as a potential theory unifying the four fundamental forces. This paper provides a historical analysis of how string theory was developed out of S-matrix physics, aiming to clarify how modern string theory, as a theory detached from experimental data, grew out of an S-matrix program that was strongly dependent upon observable quantities. Surprisingly, the theoretical practice of physicists already turned away from experiment before string theory was recast as a potential unified quantum gravity theory. With the formulation of dual resonance models (the "hadronic string theory"), physicists were able to determine almost all of the models' parameters on the basis of theoretical reasoning. It was this commitment to "non-arbitrariness", i.e., a lack of free parameters in the theory, that initially drove string theorists away from experimental input, and not the practical inaccessibility of experimental data in the context of quantum gravity physics. This is an important observation when assessing the role of experimental data in string theory.

Toward Post-Hartree-Fock Accuracy for Protein-Ligand Affinities Using the Molecules-in-Molecules Fragmentation-Based Method.

The complexity and size of large molecular systems, such as protein-ligand complexes, pose computational challenges for accurate post-Hartree-Fock calculations. This study delivers a thorough benchmarking of the Molecules-in-Molecules (MIM) method, presenting a clear and accessible strategy for layer/theory selections in post-Hartree-Fock computations on substantial molecular systems, notably protein-ligand complexes. An approach is articulated, enabling augmented computational efficiency by strategically canceling out common subsystem energy terms between complexes and proteins within the supermolecular equation. Employing DLPNO-based post-Hartree-Fock methods in conjunction with the three-layer MIM method (MIM3), this study demonstrates the achievement of protein-ligand binding energies with remarkable accuracy (errors <1 kcal mol-1), while significantly reducing computational costs. Furthermore, noteworthy correlations between theoretically computed interaction energies and their experimental equivalents were observed, with R2 values of approximately 0.90 and 0.78 for CDK2 and BZT-ITK sets, respectively, thus validating the efficacy of the MIM method in calculating binding energies. By highlighting the crucial role of diffuse or small Pople-style basis sets in the middle layer for reducing energy errors, this work provides valuable insights and practical methodologies for interaction energy computations in large molecular complexes and opens avenues for their application across a diverse range of molecular systems.

Mechanism of AGT-Mediated Repair of dG-dC Cross-Links in the Drug Resistance to Chloroethylnitrosoureas: Molecular Docking, MD Simulation, and ONIOM (QM/MM) Investigation.

Chloroethylnitrosoureas (CENUs) are important chemotherapies applied in the treatment of cancer. They exert anticancer activity by inducing DNA interstrand cross-links (ICLs) via the formation of two O6-alkylguanine intermediates, O6-chloroethylguanine (O6-ClEtG) and N1,O6-ethanoguanine (N1,O6-EtG). However, O6-alkylguanine-DNA alkyltransferase (AGT), a DNA-repair enzyme, can restore the O6-alkylguanine damages and thereby obstruct the formation of ICLs (dG-dC cross-link). In this study, the inhibitory mechanism of ICL formation was investigated to elucidate the drug resistance of CENUs mediated by AGT in detail. Based on the structures of the substrate-enzyme complexes obtained from docking and MD simulations, two ONIOM (QM/MM) models with different sizes of the QM region were constructed. The model with a larger QM region, which included the substrate (O6-ClEtG or N1,O6-EtG), a water molecule, and five residues (Tyr114, Cys145, His146, Lys165, and Glu172) in the active pocket of AGT, accurately described the repairing reaction and generated the results coinciding with the experimental outcomes. The repair process consists of two sequential steps: hydrogen transfer to form a thiolate anion on Cys145 and alkyl transfer from the O6 site of guanine (the rate-limiting step). The repair of N1,O6-EtG was more favorable than that of O6-ClEtG from both kinetics and thermodynamics aspects. Moreover, the comparison of the repairing process with the formation of dG-dC cross-link and the inhibition of AGT by O6-benzylguanine (O6-BG) showed that the presence of AGT could effectively interrupt the formation of ICLs leading to drug resistance, and the inhibition of AGT by O6-BG that was energetically more favorable than the repair of O6-ClEtG could not prevent the repair of N1,O6-EtG. Therefore, it is necessary to completely eliminate AGT activity before CENUs medication to enhance the chemotherapeutic effectiveness. This work provides reasonable explanations for the supposed mechanism of AGT-mediated drug resistance of CENUs and will assist in the development of novel CENU chemotherapies and their medication strategies.

From genome to clinic: The power of translational bioinformatics in improving human health.

Translational bioinformatics (TBI) has transformed healthcare by providing personalized medicine and tailored treatment options by integrating genomic data and clinical information. In recent years, TBI has bridged the gap between genome and clinical data because of significant advances in informatics like quantum computing and utilizing state-of-the-art technologies. This chapter discusses the power of translational bioinformatics in improving human health, from uncovering disease-causing genes and variations to establishing new therapeutic techniques. We discuss key application areas of bioinformatics in clinical genomics, such as data sources and methods used in translational bioinformatics, the impact of translational bioinformatics on human health, and how machine learning and artificial intelligence are being used to mine vast amounts of data for drug development and precision medicine. We also look at the problems, constraints, and ethical concerns connected with exploiting genomic data and the future of translational bioinformatics and its potential impact on medicine and human health. Ultimately, this chapter emphasizes the great potential of translational bioinformatics to alter healthcare and enhance patient outcomes.

Theoretical Analysis of Selectivity Differences in Ketoreductases toward Aldehyde and Ketone Carbonyl Groups.

Lactobacillus kefir alcohol dehydrogenase (LkADH) and ketoreductase from Chryseobacterium sp. CA49 (ChKRED12) exhibit different chemoselectivity and stereoselectivity toward a substrate with both keto and aldehyde carbonyl groups. LkADH selectively reduces the keto carbonyl group while retaining the aldehyde carbonyl group, producing optically pure R-alcohols. In contrast, ChKRED12 selectively reduces the aldehyde group and exhibits low reactivity toward ketone carbonyls. This study investigated the structural basis for these differences and the role of specific residues in the active site. Molecular dynamics (MD) simulations and quantum chemical calculations were used to investigate the interactions between the substrate and the enzymes and the essential cause of this phenomenon. The present study has revealed that LkADH and ChKRED12 exhibit significant differences in the structure of their respective active pockets, which is a crucial determinant of their distinct chemoselectivity toward the same substrate. Moreover, residues N89, N113, and E144 within LkADH as well as Q151 and D190 within ChKRED12 have been identified as key contributors to substrate stabilization within the active pocket through electrostatic interactions and van der Waals forces, followed by hydride transfer utilizing the coenzyme NADPH. Furthermore, the enantioselectivity mechanism of LkADH has been elucidated using quantum chemical methods. Overall, these findings not only provide fundamental insights into the underlying reasons for the observed differences in selectivity but also offer a detailed mechanistic understanding of the catalytic reaction.

Computational Investigation of the Covalent Inhibition Mechanism of Bruton's Tyrosine Kinase by Ibrutinib.

Covalent inhibitors represent a promising class of therapeutic compounds. Nonetheless, rationally designing covalent inhibitors to achieve a right balance between selectivity and reactivity remains extremely challenging. To better understand the covalent binding mechanism, a computational study is carried out using the irreversible covalent inhibitor of Bruton tyrosine kinase (BTK) ibrutinib as an example. A multi-µs classical molecular dynamics trajectory of the unlinked inhibitor is generated to explore the fluctuations of the compound associated with the kinase binding pocket. Then, the reaction pathway leading to the formation of the covalent bond with the cysteine residue at position 481 via a Michael addition is determined using the string method in collective variables on the basis of hybrid quantum mechanical-molecular mechanical (QM/MM) simulations. The reaction pathway shows a strong correlation between the covalent bond formation and the protonation/deprotonation events taking place sequentially in the covalent inhibition reaction, consistent with a 3-step reaction with transient thiolate and enolates intermediate states. Two possible atomistic mechanisms affecting deprotonation/protonation events from the thiolate to the enolate intermediate were observed: a highly correlated direct pathway involving proton transfer to the Cα of the acrylamide warhead from the cysteine involving one or a few water molecules and a more indirect pathway involving a long-lived enolate intermediate state following the escape of the proton to the bulk solution. The results are compared with experiments by simulating the long-time kinetics of the reaction using kinetic modeling.

The complexities of ligand/receptor interactions: Exploring the role of molecular vibrations and quantum tunnelling.

Molecular vibrations and quantum tunneling may link ligand binding to the function of pharmacological receptors. The well-established lock-and-key model explains a ligand's binding and recognition by a receptor; however, a general mechanism by which receptors translate binding into activation, inactivation, or modulation remains elusive. The Vibration Theory of Olfaction was proposed in the 1930s to explain this subset of receptor-mediated phenomena by correlating odorant molecular vibrations to smell, but a mechanism was lacking. In the 1990s, inelastic electron tunneling was proposed as a plausible mechanism for translating molecular vibration to odorant physiology. More recently, studies of ligands' vibrational spectra and the use of deuterated ligand analogs have provided helpful information to study this admittedly controversial hypothesis in metabotropic receptors other than olfactory receptors. In the present work, based in part on published experiments from our laboratory using planarians as an experimental organism, I will present a rationale and possible experimental approach for extending this idea to ligand-gated ion channels.